JOURNAL ARTICLE

Optimal Dynamic Advertising Policies in Digital and Traditional Channels: A Control-Theoretic Approach.

  • Published In: Information Systems Research (INFORMS), 2026, v. 37, n. 1. P. 63 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Guo, Rui; Ji, Yonghua; Jiang, Zhengrui 3 of 3

Abstract

This article investigates the optimal allocation of advertising efforts between digital and traditional channels for a monopolistic firm using optimal control theory. It explicitly models the substitution effect—where advertising in one channel reduces the marginal return of advertising in another—and incorporates different decay rates of goodwill generated by each channel through an integro-differential equation framework. The analysis reveals that digital advertising, due to its lower decay rate, always warrants investment throughout the planning horizon, while traditional advertising should be employed primarily when digital’s comparative advantage diminishes over time. Additionally, when synergistic effects between channels are considered, firms tend to adopt traditional advertising earlier and may maintain positive investment in both channels simultaneously, with the allocation ratio dynamically shifting depending on the intensity of synergy. Extensions to three-channel models confirm these findings, emphasizing the importance of dynamically adjusting advertising strategies over time to maximize profits.

Additional Information

  • Source:Information Systems Research (INFORMS). 2026/03, Vol. 37, Issue 1, p63
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:1047-7047
  • DOI:10.1287/isre.2023.0779
  • Accession Number:192724221
  • Copyright Statement:Copyright of Information Systems Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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